​
Start Simple

Question-based prompts are the best to start with. Extruct AI is finetuned to automatically determine what actions to take to fulfill your request.

Prompt examples:

  • What are the recent news associated with company X?
  • Find careers page URL of company X
  • What is the mission statement of company X?

​
Provide “How” Instructions

Some questions are easier to answer than others.

For example, What is the number of employees in company X? is more straightforward than What is the company X policy against web scraping?

When you are not satisfied with the quality of results, consider instructing Extruct on “how” to find the answer:

  • What search queries to search
  • What website pages to visit
  • What information to ignore

The example above can be improved into:

What is company X policy against web scraping?
Visit the company website and navigate to the "Terms of Service" page to find the answer.

​
Split Complex Prompts

AI Agents and LLMs often struggle with complex instructions. Thus, we suggest you split your complex task into many smaller ones.

For example, to find & analyze job openings in a company, instead of putting everything into a single task, consider splitting it like this:

  1. Find the careers page URL.
  2. Find job postings on the careers page found in Step 1.
  3. Find job postings of a company on LinkedIn (e.g. using site:linkedin.com/jobs/view intitle:<company name> filter)
  4. Analyse job postings from Step 2 and Step 3.

LLMs and autonomous AI agents are rapidly evolving areas of research. The Extruct AI team continuously monitors the best industry practices and cutting-edge research. We believe that AI agents will become increasingly autonomous in a few years as their planning and reasoning capabilities improve.

​
Customize Output Format

You can directly specify a custom format to the =EXTRUCT() function in the prompt. We usually add it at the end of the prompt. For example, here is the formatting instructions we use for c_key_people Prebuilt Agent:

...main prompt...

Output Format: plaintext bulleted list of key people. For every person, print the person's name,
position at the company, and LinkedIn profile (if available) in a format
'- <person name>, <position>: <linkedin profile>'. Avoid any additional commentary or narrative introduction.

Company Context: {input}

​
Leverage Site Filters

To ask the Agent to search specific websites, tell it to leverage site: filter. Here is an example from c_linkedin Prebuilt Agent:

Given the company context, find its LinkedIn profile URL (if it exists). Search internet with
'site:linkedin.com/company' filter. Make sure the profile found belongs to the company provided.
(e.g. matches by company name / website / other context). If you are not sure, output is Not Found.

Company context: {input}

​
Master Concatenation (&)

Do not forget spaces before and after the concatenation operator &. It won’t matter in most cases as the Agent is intelligent enough to understand the question. But it’s a good hygiene practice that might improve the quality of results at no cost.

Bad:

=EXTRUCT("Find the website of the company" & A1 & "visit the website and summarize the landing page")

Good:

=EXTRUCT("Find the website of the company " & A1 & ". Visit the website and summarize the landing page")

​
Minimize Narrative Introduction

Instruction-tuned LLMs are often trained to repeat the question in the answer, resulting in narrative introductory comments, like Here is a business model description of a company:.

To minimize this effect, we usually use one of these prompt additions:

  • Print <data you're looking for> only, nothing else.
  • Avoid any additional commentary or narrative introduction.
  • Output <data you're looking for> without any additional commentary.
Linkedin DataCredits